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- From a critical perspective, a successful film is one that receives positive reviews and is recognized for its artistic merit and storytelling prowess. However, it is important to note that success can also be measured by the impact a film has on popular culture and its ability to resonate with audiences on an emotional level.
filmsonashoestring.com/what-makes-a-film-successful/
While determining a film’s success is a complex process, a good place to start is at the box office. The number that is reported on box office charts is a film’s gross. This is the amount that a film earns before any costs are deducted, but not all of the gross comes back to the distributor.
Learn about the common methods and criteria that are used to measure a film's success, and their advantages and disadvantages.
Let’s explore the various elements that contribute to a film’s success, from a strong storyline and talented cast to effective marketing and positive audience reception. By diving into the data, we can gain insights into what truly makes a film successful in today’s competitive industry.
The success of a production has often been measured by the box office. With media playing a bigger role than ever, how do you measure a film’s success beyond the dollars?
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- Experimental Analysis, Aiming to Find Areas of Immediate Interest
- Looking at How Movie Budgets and Gross Revenue Differ Depending on Genre
- Now, Looking at How Gross Revenues Have Changed Over Time
- How Have Genre Popularities Developed Over time?
- How Do Genres Compare When Looking at Movie Popularity and Gross Revenue?
- K-Means Cluster Analysis
- K-Means Cluster Plot Visualisation
- Kable Table Displaying Information Created by The K-Means Statistical Object
- So What Did We Do and What Have We Discovered?
In order to point out areas of particular interest it’s worth creating some basic plots which are easy to interpret, this is also a good way for us to explore the structure and format of our data while recognising any immediate trends or patterns. We started by looking at columns such as movie budgets, revenue, and movie genres. From the data, it w...
From these plots alone we can see that action and adventure (with the exception of some science fiction films) excel in terms of revenue, such movies require an initial upfront budget as seen by the second plot. Now we have an idea of what variables can be useful for telling a story about commercial success, whether it be gross revenues within each...
Now that we’ve seen the trend in budgets and revenue, it’s obvious that companies are seeing the benefit of investing large sums as they create even larger returns. But what does this mean for movie popularity among viewers, as time goes on and budgets and revenues become increasingly large, are movie fans also benefiting from these huge investment...
Now looking at the same data but facetted, it’s clear to see the change in popularity of each genre over time, much clearer than the original scatter plot. Thinking analytically, you can see that movies released post 2010 are becoming increasingly popular across all genres, possibly due to modern advancements in technologies such as CGI as well as ...
Now that we have looked at genre success over the years, how can we further analyse the same set of data and tell a more descriptive story? Can we identify what companies perform best overall? With some more data processing and wrangling, we can create an object with the mean revenue, mean popularity and the number of movies released by each of the...
Our next steps were to analyse the entire data set using a unsupervised machine learning technique called K-means clustering, whereby R can arrange each movie based on mean scores for the numerical variables such as budget, revenue, popularity etc, into clusters or groups of similarity. Before doing so, we needed to create a data object of these nu...
These mean values can be seen below, byway of plotting a table using package `kable`. From this we can see what each cluster is in respects to this data. Cluster 5 being the most populated and with the lowest average scores for all of the metrics other than size. Cluster 2 seemingly the most ‘successful’ of the clusters, with high mean scores acros...
Now we have created a k-means cluster for movies based on their commercial success. While all of this information is rather interesting, it’s unclear as to what movies exactly fit inside of each of the five clusters. So with a little bit of wrangling using code in R we can assign each of the movies within the data set to their respective cluster. O...
What we’ve done here is, first experiment with some of the variables in order to find points of interest which may be calling out for further investigation. We figured commercial success was an area of particular interest so we proceeded to conduct an experimental analysis approach, we looked at budgets and revenues over time, noticing huge increas...
May 1, 2009 · Simonton suggests the following three criteria when evaluating film's success:-Critical evaluations (both pre-and post-theatrical run) -Financial performance (including first weekend and...
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After specifying some methodological caveats, the review examined the three main criteria by which a film’s success can be evaluated: critical evalua-tions (both early and post-theatrical run), financial performance (including first weekend and gross), and movie awards (including dramatic, visual, technical, and music categories).
